These data files are associated with the manuscript entitled: "Approaches for handling missing values and their impacts on biological inferences: a molecular rate case study" by Jacqueline A. May, Zen

NIAID Data Ecosystem30

Robustness to missing data.

缺失数据处理方法

模型误差评估

Results show out of sample error rate averaged over 1000 runs.

Figshare2019-10-11 更新20

Average (of S = 200 elapsed time values) processing time (in seconds) required by different algorithms to impute one dataset with 10% missing values.

缺失数据处理方法

算法评估

Average (of S = 200 elapsed time values) processing time (in seconds) required by different algorithms to impute one dataset with 10% missing values.

NIAID Data Ecosystem30

Mele0/SynLV

纵向生存预测

缺失数据处理方法

SynLV v1.0是一个用于评估纵向生存模型在不完整最终遭遇下的鲁棒性的合成基准工具。该版本包括9个主要场景和每个场景的5个队列种子。每个场景/种子目录包含训练、验证和测试集的Parquet文件以及元数据文件。数据集旨在用于在不完整最后访问情况下的鲁棒性基准测试，并使用固定的场景和分割约定进行可重复评估。

Hugging Face2026-05-04 更新30

Pseudocode for the missForestPredict algorithm - new observation.

资源简介：

相关数据集